OpenRaiser/Intern-Atlas huggingface dataset:
Millimeter-Wave Beamformed Full-dimensional MIMO Channel Estimation Based on Atomic Norm Minimization
Venue: arXiv cs.SY 2017
Year: 2017

The millimeter-wave (mmWave) full-dimensional (FD) MIMO system employs planar arrays at both the base station and user equipment and can simultaneously support both azimuth and elevation beamforming. In this paper, we propose atomic-norm-based methods for mmWave FD-MIMO channel estimation under both uniform planar arrays (UPA) and non-uniform planar arrays (NUPA). Unlike existing algorithms such as compressive sensing (CS) or subspace methods, the atomic-norm-based algorithms do not require to discretize the angle spaces of the angle of arrival (AoA) and angle of departure (AoD) into grids, thus provide much better accuracy in estimation. In the UPA case, to reduce the computational complexity, the original large-scale 4D atomic norm minimization problem is approximately reformulated as a semi-definite program (SDP) containing two decoupled two-level Toeplitz matrices. The SDP is then solved via the alternating direction method of multipliers (ADMM) where each iteration involves only closed-form computations. In the NUPA case, the atomic-norm-based formulation for channel estimation becomes nonconvex and a gradient-decent-based algorithm is proposed to solve the problem. Simulation results show that the proposed algorithms achieve better performance than the CS-based and subspace-based algorithms.
Observational Equivalence in System Estimation: Contractions in Complex Networks
Venue: arXiv cs.SY 2017
Year: 2017

Observability of complex systems/networks is the focus of this paper, which is shown to be closely related to the concept of contraction. Indeed, for observable network tracking it is necessary/sufficient to have one node in each contraction measured. Therefore, nodes in a contraction are equivalent to recover for loss of observability, implying that contraction size is a key factor for observability recovery. Here, using a polynomial order contraction detection algorithm, we analyze the distribution of contractions, studying its relation with key network properties. Our results show that contraction size is related to network clustering coefficient and degree heterogeneity. Particularly, in networks with power-law degree distribution, if the clustering coefficient is high there are less contractions with smaller size on average. The implication is that estimation/tracking of such systems requires less number of measurements, while their observational recovery is more restrictive in case of sensor failure. Further, in Small-World networks higher degree heterogeneity implies that there are more contractions with smaller size on average. Therefore, the estimation of representing system requires more measurements, and also the recovery of measurement failure is more limited. These results imply that one can tune the properties of synthetic networks to alleviate their estimation/observability recovery.
Support Spinor Machine
Venue: arXiv cs.LG 2017
Year: 2017

We generalize a support vector machine to a support spinor machine by using the mathematical structure of wedge product over vector machine in order to extend field from vector field to spinor field. The separated hyperplane is extended to Kolmogorov space in time series data which allow us to extend a structure of support vector machine to a support tensor machine and a support tensor machine moduli space. Our performance test on support spinor machine is done over one class classification of end point in physiology state of time series data after empirical mode analysis and compared with support vector machine test. We implement algorithm of support spinor machine by using Holo-Hilbert amplitude modulation for fully nonlinear and nonstationary time series data analysis.
Addressee and Response Selection in Multi-Party Conversations with Speaker Interaction RNNs
Venue: arXiv cs.CL 2017
Year: 2017

In this paper, we study the problem of addressee and response selection in multi-party conversations. Understanding multi-party conversations is challenging because of complex speaker interactions: multiple speakers exchange messages with each other, playing different roles (sender, addressee, observer), and these roles vary across turns. To tackle this challenge, we propose the Speaker Interaction Recurrent Neural Network (SI-RNN). Whereas the previous state-of-the-art system updated speaker embeddings only for the sender, SI-RNN uses a novel dialog encoder to update speaker embeddings in a role-sensitive way. Additionally, unlike the previous work that selected the addressee and response separately, SI-RNN selects them jointly by viewing the task as a sequence prediction problem. Experimental results show that SI-RNN significantly improves the accuracy of addressee and response selection, particularly in complex conversations with many speakers and responses to distant messages many turns in the past.
Enemy At the Gateways: A Game Theoretic Approach to Proxy Distribution
Venue: arXiv cs.CR 2017
Year: 2017

A core technique used by popular proxy-based circumvention systems like Tor, Psiphon, and Lantern is to secretly share the IP addresses of circumvention proxies with the censored clients for them to be able to use such systems. For instance, such secretly shared proxies are known as bridges in Tor. However, a key challenge to this mechanism is the insider attack problem: censoring agents can impersonate as benign censored clients in order to obtain (and then block) such secretly shared circumvention proxies. In this paper, we perform a fundamental study on the problem of insider attack on proxy-based circumvention systems. We model the proxy distribution problem using game theory, based on which we derive the optimal strategies of the parties involved, i.e., the censors and circumvention system operators. That is, we derive the optimal proxy distribution mechanism of a circumvention system like Tor, against the censorship adversary who also takes his optimal censorship strategies. This is unlike previous works that design ad hoc mechanisms for proxy distribution, against non-optimal censors. We perform extensive simulations to evaluate our optimal proxy assignment algorithm under various adversarial and network settings. Comparing with the state-of-the-art prior work, we show that our optimal proxy assignment algorithm has superior performance, i.e., better resistance to censorship even against the strongest censorship adversary who takes her optimal actions. We conclude with lessons and recommendation for the design of proxy-based circumvention systems.
Learning-based Control of Unknown Linear Systems with Thompson Sampling
Venue: arXiv cs.SY 2017
Year: 2017

We propose a Thompson sampling-based learning algorithm for the Linear Quadratic (LQ) control problem with unknown system parameters. The algorithm is called Thompson sampling with dynamic episodes (TSDE) where two stopping criteria determine the lengths of the dynamic episodes in Thompson sampling. The first stopping criterion controls the growth rate of episode length. The second stopping criterion is triggered when the determinant of the sample covariance matrix is less than half of the previous value. We show under some conditions on the prior distribution that the expected (Bayesian) regret of TSDE accumulated up to time T is bounded by O(\sqrt{T}). Here O(.) hides constants and logarithmic factors. This is the first O(\sqrt{T} ) bound on expected regret of learning in LQ control. By introducing a reinitialization schedule, we also show that the algorithm is robust to time-varying drift in model parameters. Numerical simulations are provided to illustrate the performance of TSDE.
Query Completion Using Bandits for Engines Aggregation
Venue: arXiv cs.IR 2017
Year: 2017

Assisting users by suggesting completed queries as they type is a common feature of search systems known as query auto-completion. A query auto-completion engine may use prior signals and available information (e.g., user is anonymous, user has a history, user visited the site before the search or not, etc.) in order to improve its recommendations. There are many possible strategies for query auto-completion and a challenge is to design one optimal engine that considers and uses all available information. When different strategies are used to produce the suggestions, it becomes hard to rank these heterogeneous suggestions. An alternative strategy could be to aggregate several engines in order to enhance the diversity of recommendations by combining the capacity of each engine to digest available information differently, while keeping the simplicity of each engine. The main objective of this research is therefore to find such mixture of query completion engines that would beat any engine taken alone. We tackle this problem under the bandits setting and evaluate four strategies to overcome this challenge. Experiments conducted on three real datasets show that a mixture of engines can outperform a single engine.
Nonsubsampled Graph Filter Banks and Distributed Implementation
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we consider nonsubsampled graph filter banks (NSGFBs) to process data on a graph in a distributed manner. Given an analysis filter bank with small bandwidth, we propose algebraic and optimization methods of constructing synthesis filter banks such that the corresponding NSGFBs provide a perfect signal reconstruction in the noiseless setting. Moreover, we prove that the proposed NSGFBs can control the resonance effect in the presence of bounded noise and they can limit the influence of shot noise primarily to a small neighborhood of its location on the graph. For an NSGFB on a graph of large size, a distributed implementation has a significant advantage, since data processing and communication demands for the agent at each vertex depend mainly on its neighboring topology. In this paper, we propose an iterative distributed algorithm to implement the proposed NSGFBs. Based on NSGFBs, we also develop a distributed denoising technique which is demonstrated to have satisfactory performance on noise suppression.
Mitigating Overexposure in Viral Marketing
Venue: arXiv cs.SI 2017
Year: 2017

In traditional models for word-of-mouth recommendations and viral marketing, the objective function has generally been based on reaching as many people as possible. However, a number of studies have shown that the indiscriminate spread of a product by word-of-mouth can result in overexposure, reaching people who evaluate it negatively. This can lead to an effect in which the over-promotion of a product can produce negative reputational effects, by reaching a part of the audience that is not receptive to it. How should one make use of social influence when there is a risk of overexposure? In this paper, we develop and analyze a theoretical model for this process; we show how it captures a number of the qualitative phenomena associated with overexposure, and for the main formulation of our model, we provide a polynomial-time algorithm to find the optimal marketing strategy. We also present simulations of the model on real network topologies, quantifying the extent to which our optimal strategies outperform natural baselines
Models and Framework for Adversarial Attacks on Complex Adaptive Systems
Venue: arXiv cs.SY 2017
Year: 2017

We introduce the paradigm of adversarial attacks that target the dynamics of Complex Adaptive Systems (CAS). To facilitate the analysis of such attacks, we present multiple approaches to the modeling of CAS as dynamical, data-driven, and game-theoretic systems, and develop quantitative definitions of attack, vulnerability, and resilience in the context of CAS security. Furthermore, we propose a comprehensive set of schemes for classification of attacks and attack surfaces in CAS, complemented with examples of practical attacks. Building on this foundation, we propose a framework based on reinforcement learning for simulation and analysis of attacks on CAS, and demonstrate its performance through three real-world case studies of targeting power grids, destabilization of terrorist organizations, and manipulation of machine learning agents. We also discuss potential mitigation techniques, and remark on future research directions in analysis and design of secure complex adaptive systems.
Data offloading in mobile edge computing: A coalitional game based pricing approach
Venue: arXiv cs.GT 2017
Year: 2017

Mobile edge computing (MEC), affords service to the vicinity of mobile devices (MDs), has become a key technology for future network. Offloading big data to the MEC server for preprocessing is a attractive choice of MDs. In the paper, we investigate data offloading from MDs to MEC servers. A coalitional game based pricing scheme is proposed. We apply coalitional game to depict the offloading relationship between MDs and MEC servers, and utilize pricing as the stimuli for the offloading. A scheduled MD chooses one MEC server within the same coalition for offloading, and pays the selected MEC server for the MEC service. We formulate a coalitional game, where MDs and MEC servers are players and their utilities are respectively defined. Next, we analyze the formulated game. Specially, the core is studied. Finally, utility performance of the proposed scheme under the 2-MD and 2-MEC- server scenario are demonstrated.
On The Parameterized Tractability of the Just-In-Time Flow-Shop Scheduling Problem
Venue: arXiv cs.DS 2017
Year: 2017

Since its development in the early 90's, parameterized complexity has been widely used to analyze the tractability of many NP-hard combinatorial optimization problems with respect to various types of problem parameters. While the generic nature of the framework allows the analysis of any combinatorial problem, the main focus along the years was on analyzing graph problems. In this paper we diverge from this trend by studying the parameterized complexity of Just-In-Time (JIT) flow-shop scheduling problems. Our analysis focuses on the case where the number of due dates is considerably smaller than the number of jobs, and can thus be considered as a parameter. We prove that the two-machine problem is W[1]-hard with respect to this parameter, even if all processing times on the second machine are of unit length, while the problem is in XP even for a parameterized number of machines. We then move on to study the tractability of the problem when combining the different number of due dates with either the number of different weights or the number of different processing times on the first machine. We prove that in both cases the problem is fixed-parameter tractable for the two machine case, and is W[1]-hard for three or more machines.
A Survey of Calibration Methods for Optical See-Through Head-Mounted Displays
Venue: arXiv cs.HC 2017
Year: 2017

Optical see-through head-mounted displays (OST HMDs) are a major output medium for Augmented Reality, which have seen significant growth in popularity and usage among the general public due to the growing release of consumer-oriented models, such as the Microsoft Hololens. Unlike Virtual Reality headsets, OST HMDs inherently support the addition of computer-generated graphics directly into the light path between a user's eyes and their view of the physical world. As with most Augmented and Virtual Reality systems, the physical position of an OST HMD is typically determined by an external or embedded 6-Degree-of-Freedom tracking system. However, in order to properly render virtual objects, which are perceived as spatially aligned with the physical environment, it is also necessary to accurately measure the position of the user's eyes within the tracking system's coordinate frame. For over 20 years, researchers have proposed various calibration methods to determine this needed eye position. However, to date, there has not been a comprehensive overview of these procedures and their requirements. Hence, this paper surveys the field of calibration methods for OST HMDs. Specifically, it provides insights into the fundamentals of calibration techniques, and presents an overview of both manual and automatic approaches, as well as evaluation methods and metrics. Finally, it also identifies opportunities for future research. % relative to the tracking coordinate system, and, hence, its position in 3D space.
Mesh-based Autoencoders for Localized Deformation Component Analysis
Venue: arXiv cs.GR 2017
Year: 2017

Spatially localized deformation components are very useful for shape analysis and synthesis in 3D geometry processing. Several methods have recently been developed, with an aim to extract intuitive and interpretable deformation components. However, these techniques suffer from fundamental limitations especially for meshes with noise or large-scale deformations, and may not always be able to identify important deformation components. In this paper we propose a novel mesh-based autoencoder architecture that is able to cope with meshes with irregular topology. We introduce sparse regularization in this framework, which along with convolutional operations, helps localize deformations. Our framework is capable of extracting localized deformation components from mesh data sets with large-scale deformations and is robust to noise. It also provides a nonlinear approach to reconstruction of meshes using the extracted basis, which is more effective than the current linear combination approach. Extensive experiments show that our method outperforms state-of-the-art methods in both qualitative and quantitative evaluations.
ProSLAM: Graph SLAM from a Programmer's Perspective
Venue: arXiv cs.RO 2017
Year: 2017

In this paper we present ProSLAM, a lightweight stereo visual SLAM system designed with simplicity in mind. Our work stems from the experience gathered by the authors while teaching SLAM to students and aims at providing a highly modular system that can be easily implemented and understood. Rather than focusing on the well known mathematical aspects of Stereo Visual SLAM, in this work we highlight the data structures and the algorithmic aspects that one needs to tackle during the design of such a system. We implemented ProSLAM using the C++ programming language in combination with a minimal set of well known used external libraries. In addition to an open source implementation, we provide several code snippets that address the core aspects of our approach directly in this paper. The results of a thorough validation performed on standard benchmark datasets show that our approach achieves accuracy comparable to state of the art methods, while requiring substantially less computational resources.
A Review of Evaluation Techniques for Social Dialogue Systems
Venue: arXiv cs.CL 2017
Year: 2017

In contrast with goal-oriented dialogue, social dialogue has no clear measure of task success. Consequently, evaluation of these systems is notoriously hard. In this paper, we review current evaluation methods, focusing on automatic metrics. We conclude that turn-based metrics often ignore the context and do not account for the fact that several replies are valid, while end-of-dialogue rewards are mainly hand-crafted. Both lack grounding in human perceptions.
Extending LTE into the Unlicensed Spectrum: Technical Analysis of the Proposed Variants
Venue: arXiv cs.NI 2017
Year: 2017

The commercial success of the Long Term Evolution (LTE) and the resulting growth in mobile data demand have urged cellular network operators to strive for new innovations. LTE in unlicensed spectrum has been proposed to allow cellular network operators to offload some of their data traffic by accessing the unlicensed 5 GHz frequency band. Currently, there are three proposed variants for LTE operation in the unlicensed band, namely LTE-U, Licensed Spectrum Access (LAA), and MulteFire. This paper provides a comparative analysis of these variants and explains the current regulations of the 5 GHz band in different parts of the world. We present the technical details of the three proposed versions and analyze them in terms of their operational features and coexistence capabilities to provide an R\&D perspective for their deployment and coexistence with legacy systems.
A Computational Model of Afterimages based on Simultaneous and Successive Contrasts
Venue: arXiv cs.CV 2017
Year: 2017

Negative afterimage appears in our vision when we shift our gaze from an over stimulated original image to a new area with a uniform color. The colors of negative afterimages differ from the old stimulating colors in the original image when the color in the new area is either neutral or chromatic. The interaction between stimulating colors in the test and inducing field in the original image changes our color perception due to simultaneous contrast, and the interaction between changed colors perceived in the previously-viewed field and the color in the currently-viewed field also affects our perception of colors in negative afterimages due to successive contrast. Based on these observations we propose a computational model to estimate colors of negative afterimages in more general cases where the original stimulating color in the test field is chromatic, and the original stimulating color in the inducing field and the new stimulating color can be either neutral or chromatic. We validate our model with human experiments.
An Operational Semantic Basis for OpenMP Race Analysis
Venue: arXiv cs.DC 2017
Year: 2017

OpenMP is the de facto standard to exploit the on-node parallelism in new generation supercomputers.Despite its overall ease of use, even expert users are known to create OpenMP programs that harbor concurrency errors, of which one of the most insidious of errors are {\em data races}.OpenMP is also a rapidly evolving standard, which means that future data races may be introduced within unfamiliar contexts.A simple and rigorous operational semantics for OpenMP can help build reliable race checkers and ward off future errors through programmer education and better tooling.This paper's key contribution is a simple operational semantics for OpenMP, with primitive events matching those generated by today's popular OpenMP runtimes and tracing methods such as OMPT.This makes our operational semantics more than a theoretical document for intellectual edification; it can serve as a blueprint for OpenMP event capture and tool building.We back this statement by summarizing the workings of a new data race checker for OpenMP being built based on this semantics.The larger purpose served by our semantics is to serve the needs of the OpenMP community with regard to their contemplated extensions to OpenMP, as well as future tooling efforts.
MOLTE: a Modular Optimal Learning Testing Environment
Venue: arXiv cs.LG 2017
Year: 2017

We address the relative paucity of empirical testing of learning algorithms (of any type) by introducing a new public-domain, Modular, Optimal Learning Testing Environment (MOLTE) for Bayesian ranking and selection problem, stochastic bandits or sequential experimental design problems. The Matlab-based simulator allows the comparison of a number of learning policies (represented as a series of .m modules) in the context of a wide range of problems (each represented in its own .m module) which makes it easy to add new algorithms and new test problems. State-of-the-art policies and various problem classes are provided in the package. The choice of problems and policies is guided through a spreadsheet-based interface. Different graphical metrics are included. MOLTE is designed to be compatible with parallel computing to scale up from local desktop to clusters and clouds. We offer MOLTE as an easy-to-use tool for the research community that will make it possible to perform much more comprehensive testing, spanning a broader selection of algorithms and test problems. We demonstrate the capabilities of MOLTE through a series of comparisons of policies on a starter library of test problems. We also address the problem of tuning and constructing priors that have been largely overlooked in optimal learning literature. We envision MOLTE as a modest spur to provide researchers an easy environment to study interesting questions involved in optimal learning.